Models for Geostatistical Binary Data: Properties and Connections
From MaRDI portal
Publication:5869252
DOI10.1080/00031305.2018.1444674OpenAlexW2875341772MaRDI QIDQ5869252
Publication date: 28 September 2022
Published in: The American Statistician (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00031305.2018.1444674
probit modelgeneralized linear mixed modelGaussian copula modelindicator krigingclipped Gaussian random field
Related Items (4)
Bivariate small‐area estimation for binary and gaussian variables based on a conditionally specified model ⋮ On Some Characteristics of Gaussian Covariance Functions ⋮ Projecting flood-inducing precipitation with a Bayesian analogue model ⋮ Variational Bayes Estimation of Discrete-Margined Copula Models With Application to Time Series
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Matching conditional and marginal shapes in binary random intercept models using a bridge distribution function
- Data augmentation strategies for the Bayesian spatial probit regression model
- A generalized linear mixed model for longitudinal binary data with a marginal logit link function
- An introduction to copulas.
- Model-based geostatistics.
- Computation of multivariate normal and \(t\) probabilities
- On measuring internal dependence in a set of random variables
- Parameter estimation for excursion set texture models
- An empirical comparison of kriging methods for nonlinear spatial point prediction
- Bayesian prediction of clipped Gaussian random fields.
- Copula-based geostatistical modeling of continuous and discrete data including covariates
- Maximum likelihood estimation of regression parameters with spatially dependent discrete data
- Analysis of binary spatial data by quasi-likelihood estimating equations
- Indicator variogram models: do we have much choice?
- Efficient estimation and prediction for the Bayesian binary spatial model with flexible link functions
- Bridging Conditional and Marginal Inference for Spatially Referenced Binary Data
- Efficient pairwise composite likelihood estimation for spatial-clustered data
- On Estimation and Prediction for Spatial Generalized Linear Mixed Models
- Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models
- Childhood Malaria in the Gambia: A Case-Study in Model-Based Geostatistics
- Model-Based Geostatistics
- A Composite Likelihood Approach to Binary Spatial Data
- A note on the correlation structure of transformed Gaussian random fields
- Random Effects Modeling of Multiple Binomial Responses Using the Multivariate Binomial Logit‐Normal Distribution
- A Generalized Estimating Equations Approach for Spatially Correlated Binary Data: Applications to the Analysis of Neuroimaging Data
- Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
- On the Correlation Structure of Gaussian Copula Models for Geostatistical Count Data
- Analyzing Spatially Distributed Binary Data Using Independent‐Block Estimating Equations
This page was built for publication: Models for Geostatistical Binary Data: Properties and Connections